/tools

FragNet is a Graph Neural Network that predicts molecular properties while providing interpretability insights into how substructures affect predictions. It supports various molecular types and includes functionalities for data creation and visualization.

AlphaSeq_Antibody_Dataset contains two datasets with quantitative binding scores of scFv-format antibodies against a SARS-CoV-2 target peptide. It is designed to support protein representation learning and includes data for machine learning optimization of antibody candidates.

CASTER is a tool designed to predict drug interactions by utilizing chemical substructure representations. It provides a framework for understanding how different molecular structures can influence drug interactions, making it relevant for drug discovery efforts.

cg_openmm is a Python package designed for setting up and analyzing coarse-grained foldamer replica exchange molecular dynamics simulations using OpenMM. It includes features for structural analysis and thermodynamic evaluation, making it a valuable tool for researchers in computational chemistry.

The TABASCO repository presents a fast and simplified model for generating molecules with improved physical quality. It utilizes a non-equivariant Transformer architecture to treat molecular generation as a sequence modeling problem, making it efficient for generating small molecules.

The Drug Design Data Resource (D3R) is a suite of software designed to facilitate the filtering and scoring of new molecular sequences. It supports participants in the CELPP challenge by providing necessary workflows and tools for molecular docking and analysis.

MPEK is a multi-task learning tool that predicts enzyme turnover number (kcat) and Michaelis-Menten constant (Km) using enzyme sequences and substrate SMILES. It aims to enhance the evaluation of enzymatic efficiency and supports applications in biocatalysis and drug discovery.

AbLang2 is an antibody-specific language model that addresses germline bias in antibody sequences to improve antibody design. It generates amino acid likelihoods and residue embeddings, facilitating the prediction of mutations that enhance binding properties.

PEGgenerator is a tool designed to generate PEG topology for molecular dynamics simulations in GROMACS and LAMMPS. It allows users to create configurations for PEG molecules with varying numbers of monomers and perform energy minimization and relaxation simulations.

NMRforMD is a Python script designed to compute dipolar NMR relaxation times (T1 and T2) from molecular dynamics trajectory files. It works with various simulation packages and includes datasets for practical use in NMR analysis.

The ChIMES calculator provides a flexible toolset for evaluating interactions using the Chebyshev Interaction Model for Efficient Simulation (ChIMES), which is a machine-learned interatomic potential. It is applicable in various molecular simulations and can be integrated into other codes for advanced computational chemistry tasks.

MCGLPPI is a tool designed for predicting the properties of protein-protein complexes by integrating a molecular coarse-grained model into a geometric representation learning framework. It facilitates the generation of required data for model pre-training and downstream predictions, specifically targeting protein-protein interaction properties.

GatorAffinity is a geometric deep learning model that predicts protein-ligand binding affinities by leveraging a large-scale synthetic dataset of protein-ligand complexes. It is pre-trained on extensive datasets and fine-tuned with experimental structures to enhance prediction accuracy.

CAPLA is a deep learning tool designed to improve the prediction of protein-ligand binding affinity by utilizing a cross-attention mechanism. It aims to enhance the accuracy and speed of binding affinity predictions, which is crucial for drug development.

Affinity2Vec is a tool designed for predicting drug-target binding affinities through the use of representation learning and graph mining techniques. It includes datasets and models for training and evaluating predictions related to molecular interactions.

QuickBind is a light-weight and interpretable molecular docking model that predicts binding affinities for molecular complexes. It utilizes a dataset from PDBBind for training and evaluation, making it a valuable tool for researchers in drug discovery and molecular design.

AI4PFAS is a repository that provides a dataset and code for predicting the toxicity of PFAS compounds using uncertainty-informed deep transfer learning. It includes various models and benchmarks for assessing toxicity, specifically focusing on LD50 values.

LigParGen is a repository that provides tutorials and FAQs about the LigParGen Server, which is used for generating ligand parameters for molecular dynamics simulations. It focuses on the application of force fields in molecular simulations.

Screenlamp is a Python toolkit designed to facilitate ligand-based virtual screening workflows. It enables researchers to prioritize ligand candidates through hypothesis-driven filtering, contributing to drug discovery efforts.

V2DB is a database and tool for generating and predicting properties of novel two-dimensional materials through virtual screening. It utilizes machine learning models to predict various material properties, facilitating the discovery of stable and functional 2D materials.

PolyFold is an interactive visual simulator designed for distance-based protein folding. It allows users to visualize and manipulate the folding process in real-time, utilizing stochastic optimization algorithms to achieve protein structure predictions.

DiffPALM is a tool for pairing interacting protein sequences using masked language modeling. It employs a differentiable pairing method to optimize multiple sequence alignments, which is crucial for understanding protein interactions and structures.